Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

N. Czink, P. Cera, J. Salo, E. Bonek, J.P. Nuutinen, J. Ylitalo:
"A framework for automatic clustering of parametric MIMO channel data including path powers";
Vortrag: IEEE Vehicular Technology Conference (VTC), Montreal, Kanada; 25.09.2006 - 28.09.2006; in: "IEEE Vehicular Technology Conference Fall 2006", J. Ylitalo (Hrg.); (2006), 5 S.

Kurzfassung englisch:
Abstract-We present a solution to the problem of identifying clusters from MIMO measurement data in a data window, with a minimum of user interaction. Conventionally, visual inspection has been used for the cluster identification. However this approach is impractical for a large amount of measurement data. Moreover, visual methods lack an accurate definition of a "cluster" itself. We introduce a framework that is able to cluster multi-path components (MPCs), decide on the number of clusters, and discard outliers. For clustering we use the K-means algorithm, which iteratively moves a number of cluster centroids through the data space to minimize the total difference between MPCs and their closest centroid. We significantly improve this algorithm by following changes: (i) as the distance metric we use the multipath component distance (MCD), (ii) the distances are weighted by the powers of the MPCs. The implications of these changes result in a definition of a "cluster" itself that appeals to intuition. We assess the performance of the new algorithm by clustering real-world measurement data from an indoor big hall environment.

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